Screening of knee-joint vibroarthrographic signals using time and spectral domain features

Shidore, Mrunal; Shreeram, Athreya,; Deshpande, Shantanu; Jalnekar, Rajesh

doi:10.1016/j.bspc.2021.102808

Cited by 15 publications

(2 citation statements)

References 31 publications

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“…This high accuracy can be attributed to fractal data analysis, which was implemented in those studies. The authors state that their results also differ from those published by Shidore et al (2021) in regard to SVM classification. In their study, the reported AUC reached 0.926, while in the present study it was only 0.756 and the lowest accuracy was found using NBC.…”

Section: Resultscontrasting

confidence: 68%

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

KARPIŃSKI,

KRAKOWSKI,

JONAK

et al. 2023

Appl. Comput. Sci.

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Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

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Section: Resultscontrasting

confidence: 68%

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

KARPIŃSKI,

KRAKOWSKI,

JONAK

et al. 2023

Appl. Comput. Sci.

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“…So far, a number of studies have been carried out in the field on knee VAG signal identification and analysis [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. These studies show that, the use of accelerometers was preferred to stethoscopes and microphones because of their insufficient low frequency responses in defining knee related problems.…”

Section: Introductionmentioning

confidence: 99%

Biyomedikal Uygulamalar için Çok Noktalı Çok Boyutlu İvmeölçer Veri Kayıt Sistemi

Akkan

Şenol

ÖZGÖREN

2022

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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Bazı biyomedikal sinyal işleme uygulamaları, özel veri kaydı donanımı gerektirir. Diz ile ilgili non-invaziv teşhis ve çene ile ilgili elektroensefalografi (EEG) bozunum temizleme uygulamaları, eşzamanlı çok kanallı titreşim veri kaydı gerektiren iyi adaylar olacaktır. Bu çalışmada, yeni bir çok noktalı çok boyutlu ivme veri kayıt sistemi önerilmiştir. Bu sistem ivmeölçerler kullanarak aynı anda üç farklı noktadan üç boyutlu eğilim ve titreşim verilerini toplamaktadır. Çok kanallı sinyal analizi, sensör verilerini filtrelemek ve bileşenlerine ayırmak için eşzamanlı veri kayıtları gerektirir. Seçilen ivmeölçer, aynı anda üç eksen veri kaydı gerekliliğini sağlamaktadır. İvmeölçer veri kayıt sistemi, teşhis desteği için dizden ve EEG çene bozunum temizleme desteği için çeneden eğilim ve titreşim verileri elde etmek için kullanılabilir. Diz hareketlerinin titreşimlerini (vibroartrografik (VAG) sinyaller) tespit etmek için diz kapağı ve yan pozisyonlara üç ivmeölçer yerleştirilebilir. Elde edilen VAG sinyalleri, istatiksel veya zaman-frekans analiz teknikleri ile değerlendirilebilir. Ayrıca çene ve boyun hareketlerini kaydetmek için yüze üç adet ivmeölçer yerleştirilmiştir. Eş zamanlı olarak kaydedilen EEG ve çene verileri, istenmeyen boyun ve çene bozunumlarını çıkarmak için filtreleme veya istatistik yöntemler ile daha ileri bir şekilde analiz edilebilir.

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KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method

Yang

2022

Communications in Computer and Information Science

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Screening of knee-joint vibroarthrographic signals using time and spectral domain features

Cited by 15 publications

References 31 publications

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

Comparison of Selected Classification Methods Based on Machine Learning as a Diagnostic Tool for Knee Joint Cartilage Damage Based on Generated Vibroacoustic Processes

Biyomedikal Uygulamalar için Çok Noktalı Çok Boyutlu İvmeölçer Veri Kayıt Sistemi

KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method

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